The aim is to train deep neural networks – aka to calibrate the parameters
of interaction potentials – with a method that scales to large
datasets. The deep learning community uses stochastic optimization methods
that operate on
batches of data and never on the whole dataset at once.
While traditional Social Force models are calibrated with second order
methods (e.g. [1]}, [2]}),
we aim to use the first order Stochastic Gradient Descent (SGD) [3]}.
SGD can make use of the gradients that our
differentiable simulation provides and allows to operate on small batches of data.
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